The Rise of AI Art

Over the past ten years, Artificial Intelligence (AI) and Machine Learning (ML) have steadily crept into the Art Industry. From Deepfakes to DALL·E, the impact of these new technologies can be longer be ignored, and many communities are now on the edge of a reckoning. On one side, the potential for modern AIs to generate and edit both images and videos is opening new job opportunities for millions; but on the other is also threatening a sudden and disruptive change across many industries.

The purpose of this long article is to serve as an introduction to the complex topic of AI Art: from the technologies that are powering this revolution, to the ethical and legal issues they have unleashed. While this is still an ongoing conversation, I hope it will serve as a primer for anyone interested in better understanding these phenomena—especially journalists who are keen to learn more about the benefits, changes and challenges that that AI will inevitably bring into our own lives. And since the potential of these technologies—and the best way to use them—are still being explored, there will likely be more questions and tentative suggestions, rather than definite answers.

In this article I will try to keep a positive outlook, as I feel is important to show and inspire people on how to better harness this technology, rather than just demonising it. And while predicting the future is beyond the scope of this article, there will be plenty of examples of how new art practices and technologies have impacted art communities in the past.

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Car Paint Shader: Thin-Film Interference

This post completes the journey started in The Mathematics of Thin-Film Interference, by explaining how to turn the equations previously presented into actual shader code.

You can find the complete series here:

A link to download the Unity project used in this series is also provided at the end of the page.

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The Mathematics of Thin-Film Interference

This post continues our journey through the Mathematical foundations of iridescence. This time, we will discuss a new way in which material can split light: thin-film interference. This is how bubbles (and car paint) get their unique reflections.

You can find the complete series here:

A link to download the Unity project used in this series is also provided at the end of the page.

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The Extended Kalman Filter

This is the third part of the series dedicated to one of the most popular sensor de-noising technique: Kalman filters. This article will explain how to model non-linear processes to improve the filter performance, something known as the Extended Kalman Filter.

You can read all the tutorials in this online course here:

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Modelling Kalman Filters

This is the third part of the series dedicated to one of the most popular sensor de-noising technique: Kalman filters. This article will explain how to model processes to improve the filter performance.

You can read all the tutorials in this online course here:

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The Mathematics of the Kalman Filter

This is the second part of the series dedicated to one of the most popular sensor de-noising technique: Kalman filters. This article will introduce the Mathematics of the Kalman Filter, with a special attention to a quantity that makes it all possible: the Kalman gain.

You can read all the articles in this online course here:

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Kalman Filters: From Theory to Implementation

This series of articles will introduce the Kalman filter, a powerful technique that is used to reduce the impact of noise in sensors. If you are working with Arduino, this tutorial will teach you how to reliably read data from your sensors. This is a tutorial that will be very helpful even if you are not working with hardware: game developers are often challenged by noise, especially when it comes to integrating data collected from gyroscopes and accelerometers. And even if you are not building a mobile game, you can use Kalman filters to increase the precision of your controllers.

This first post will focus on a brief introduction to the problem, while the other tutorials in this online will focus on the derivation and implementation of a Kalman filter.

You can read all the tutorials in this online course here:

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The World Generation of Minecraft

This is a companion article to the documentary about the world generation of Minecraft, which you can see below. This is a chance to expand on the content, including more information and resources that was not possible to include in the original 45 minutes of the video.

Have you ever wondered how many grains of sand are on this planet? Well …a rough estimate is… over 7 quintillion! That’s a 7 followed by 18 zeros. And yet, that’s not even half the number of the unique words in Minecraft. So how does Minecraft—and other games like it—build such complex, beautifully crafted yet fully procedural worlds? This article will explore how the game generates its worlds: from its tallest mountain, to its deepest cave. Welcome to the World Generation of Minecraft.

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Topographical Maps in Unity: Edge Detection

This tutorial will teach you how to recreate a very popular effect in games: topographical maps.

This is a two-part series, which will cover all the necessary aspects—from the Maths to the shader code—to make this possible:

In this second part, we will focus on the edge detection algorithm that will be used to draw the contours of the terrain.

A link to download the full Unity package is also available at the end of the tutorial.

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Topographical Maps in Unity: Terrain Shading

This tutorial will teach you how to recreate a very popular effect in games: topographical maps.

This is a two-part series, which will cover all the necessary aspects—from the Maths to the shader code—to make this possible:

A link to download the full Unity package is also available at the end of the tutorial.

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